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Background The assessment and characterization of the gut microbiome has become a focus of research in the area of human autoimmune diseases. Ankylosing spondylitis is an inflammatory autoimmune disease and evidence showed that ankylosing spondylitis may be a microbiome-driven disease. Results To investigate the relationship between the gut microbiome and ankylosing spondylitis, a quantitative metagenomics study based on deep shotgun sequencing was performed, using gut microbial DNA from 211 Chinese individuals. A total of 23,709 genes and 12 metagenomic species were shown to be differentially abundant between ankylosing spondylitis patients and healthy controls. Patients were characterized by a form of gut microbial dysbiosis that is more prominent than previously reported cases with inflammatory bowel disease. Specifically, the ankylosing spondylitis patients demonstrated increases in the abundance of Prevotella melaninogenica , Prevotella copri , and Prevotella sp. C561 and decreases in Bacteroides spp. It is noteworthy that the Bifidobacterium genus, which is commonly used in probiotics, accumulated in the ankylosing spondylitis patients. Diagnostic algorithms were established using a subset of these gut microbial biomarkers. Conclusions Alterations of the gut microbiome are associated with development of ankylosing spondylitis. Our data suggest biomarkers identified in this study might participate in the pathogenesis or development process of ankylosing spondylitis, providing new leads for the development of new diagnostic tools and potential treatments.

ObjectiveThis article aims to investigate the changes of T helper 17 (Th17) cells, regulatory T (Treg) cells and their associated cytokines in patients with systemic lupus erythematosus (SLE).MethodsMultiple databases were investigated to identify articles that explored Th17 cells, Treg cells and relevant cytokines in SLE patients. A random effects model was used for calculating pooled standardized mean differences. Stata version 15.0 was utilized to conduct the meta-analysis.ResultsThe levels of Th17 cells, IL-17, IL-6, IL-21 and IL-10 were higher in SLE patients than in healthy controls (HCs), but the TGF-β levels were lower. The percentage of Treg cells was lower than HCs in SLE individuals older than 33. Among studies that had 93% or lower females, the percentage of Th17 cells was greater in patients than in HCs. However, the percentage of Treg cells was lower when the proportion of females was less than 90%. Patients with lupus nephritis or active SLE had an increased proportion of Th17 cells and a decreased proportion of Treg cells.ConclusionsThe increased level of Th17 cells and related cytokines could be the main reason for the elevated Th17/Treg ratio in SLE. The percentages of Th17 and Treg cells were associated with gender, age, disease activity and kidney function. Furthermore, the reduced proportions of Treg cells may primarily result in a rise in the Th17/Treg ratio in older or active SLE patients.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42023454937.

Transient stability preventive control (TSPC), a method to efficiently withstand the severe contingencies in a power system, is mathematically a transient stability constrained optimal power flow (TSC-OPF) issue, attempting to maintain the economical and secure dispatch of a power system via generation rescheduling. The traditional TSC-OPF issue incorporated with differential-algebraic equations (DAE) is time consumption and difficult to solve. Therefore, this paper proposes a new TSPC method driven by a naturally inspired optimization algorithm integrated with transient stability assessment. To avoid solving complex DAE, the stacking ensemble multilayer perceptron (SEMLP) is used in this research as a transient stability assessment (TSA) model and integrated into the optimization algorithm to replace transient stability constraints. Therefore, less time is spent on challenging calculations. Simultaneously, sensitivity analysis (SA) based on this TSA model determines the adjustment direction of the controllable generators set. The results of this SA can be utilized as prior knowledge for subsequent optimization algorithms, thus further reducing the time consumption process. In addition, a naturally inspired algorithm, Aptenodytes Forsteri Optimization (AFO), is introduced to find the best operating point with a near-optimal operational cost while ensuring power system stability. The accuracy and effectiveness of the method are verified on the IEEE 39-bus system and the IEEE 300-bus system. After the implementation of the proposed TSPC method, both systems can ensure transient stability under a given contingency. The test experiment using AFO driven by SEMLP and SA on the IEEE 39-bus system is completed in about 35 s, which is one-tenth of the time required by the time domain simulation method.

Background Rheumatoid arthritis, a chronic autoimmune disease, is characterized by synovial hyperplasia and cartilage erosion. Here, we investigated the potential mechanism of action of quercetin, the main component of flavonoids, in treating rheumatoid arthritis. Object To examine the anti-arthritic effects of quercetin and elucidate the specific mechanisms that differentiate its metabolic effects on autoimmune and inflammatory responses at the synovial cell level. Methods We created a collagen-induced arthritis (CIA) model in Wistar rats, which were administered quercetin (50 or 100 mg/kg) continuously for four weeks via stomach perfusion. The arthritis score, histopathological staining, radiological assessment, and serum biochemical parameters were used to study the impact of quercetin on disease improvement. Additionally, immunofluorescence was employed to detect JAK1/STAT3/HIF-1α expression in rat joints. Moreover, the effects of quercetin (20, 40, and 80 µmol/L) on the properties and behavior of synovial fibroblasts were evaluated in an in vitro MH7A cell model using flow cytometry, CCK8, and transwell assays. Further, the mRNA expression levels of inflammatory cytokines IL1β, IL6, IL17, and TNFα were assessed by quantitative real-time PCR. Glucose, lactate, lactate dehydrogenase, pyruvate, pyruvate dehydrogenase, and adenosine triphosphate assay kits were employed to measure the metabolic effects of quercetin on synovial fibroblasts. Finally, immunoblotting was used to examine the impact of quercetin on the JAK1/STAT3/HIF-1α signaling pathway in synovial fibroblasts. Results In vivo experiments confirmed the favorable effects of quercetin in CIA rats, including an improved arthritis score and reduced ankle bone destruction, in addition to a decrease in the pro-inflammatory cytokines IL-1β, IL-6, IL-17, and TNF-α in serum. Immunofluorescence verified that quercetin may ameliorate joint injury in rats with CIA by inhibiting JAK1/STAT3/HIF-1α signaling. Various in vitro experiments demonstrated that quercetin effectively inhibits IL-6-induced proliferation of MH7A cells and reduces their migratory and invasive behavior, while inducing apoptosis and reducing the expression of the pro-inflammatory cytokines IL1β, IL6, IL17, and TNFα at the mRNA level. Quercetin caused inhibition of glucose, lactate, lactate dehydrogenase, pyruvate, and adenosine triphosphate and increased pyruvate dehydrogenase expression in MH7A cells. It was further confirmed that quercetin may inhibit energy metabolism and inflammatory factor secretion in MH7A cells through JAK1/STAT3/HIF-1α signaling. Conclusions Quercetin’s action on multiple target molecules and pathways makes it a promising treatment for cartilage injury in rheumatoid arthritis. By reducing joint inflammation, improving joint metabolic homeostasis, and decreasing immune system activation energy, quercetin inhibits the JAK1/STAT3/HIF-1α signaling pathway to improve disease status.

Objective This meta-analysis aims to explore the potential link between vaccines and systemic lupus erythematosus (SLE). Methods We systematically searched PubMed, Cochrane Library, and Embase for observational studies from inception to September 3, 2023, using medical subject headings (MeSH) and keywords. Study quality was assessed using the NOS scale. Statistical analyses were conducted using STATA software (version 14.0). Publication bias was evaluated using funnel plots and Egger’s regression. Results The meta-analysis incorporated 17 studies, encompassing 45,067,349 individuals with follow-up periods ranging from 0.5 to 2 years. The pooled analysis revealed no significant association between vaccinations and an increased risk of SLE [OR = 1.14, 95% CI (0.86–1.52), I 2  = 78.1%, P  = 0.348]. Subgroup analyses indicated that HBV vaccination was significantly associated with an elevated risk of SLE [OR =2.11, 95% CI (1.11-4.00), I 2  = 63.3%, P  = 0.02], HPV vaccination was slightly associated with an increased risk of SLE [OR = 1.43, 95% CI (0.88–2.31), I 2  = 72.4%, P  = 0.148], influenza vaccination showed no association with an increased risk of SLE [OR = 0.96, 95% CI (0.82–1.12), I 2  = 0.0%, P  = 0.559], and COVID-19 vaccine was marginally associated with a decreased risk of SLE [OR = 0.44, 95% CI (0.18–1.21), I 2  = 91.3%, P  = 0.118]. Conclusions This study suggests that vaccinations are not linked to an increased risk of SLE. Our meta-analysis results provide valuable insights, alleviating concerns about SLE risk post-vaccination and supporting further vaccine development efforts.

ObjectiveTo quantify rheumatoid arthritis (RA) cases attributable to selected non-genetic risk factors.DesignNational Health and Nutrition Examination Survey (NHANES) and meta-analysis.ParticipantsUS adults.Data sourcesThe prevalence of exposure was obtained from NHANES. Weighted analysis was performed to account for the complex sampling design in NHANES. PubMed and Web of Science up to 31 March 2019 were searched to identify epidemiological studies reported the association between non-genetic risk factors and RA in US adults. Relative risk (RR) value and the corresponding CI were pooled by meta-analysis to evaluate the associations between modifiable risk factors and RA. Population attributable fraction (PAF) was calculated based on the prevalence and RR data.ResultsThe weighted percentages of former smokers, current smokers and overweight or obese people were 24.84%, 23.93% and 63.97%, and the average alcohol consumption was 51.34 g/week. In the meta-analysis, we found that former smokers (RR 1.22, 95% CI 1.10 to 1.36) and current smokers (RR 1.47, 95% CI 1.29 to 1.68) had higher risks of RA. Overweight and obese individuals had 1.27-fold (95% CI 1.09 to 1.48) increased risk of RA. Each per 50 g/week increment of alcohol consumption was associated with 8% (95% CI 0% to 16%) reduction in the risk of RA. Therefore, PAF value of smoking was 14.00% (95% CI 8.13% to 23.33%). Excess body mass index (BMI) was found to account for 14.73% (95% CI 5.45% to 23.50%) of RA incidence. The fraction of RA risk attributed by low alcohol intake was 8.21% (95% CI 0.31% to 16.39%). Collectively, we found that 32.69% (95% CI 13.41% to 50.96%) of RA cases were attributable to smoking, overweight or obesity and low alcohol drinking.ConclusionNearly 33% of RA incidence was attributed to smoking, excess BMI and low alcohol drinking in USA. Our findings could provide a basis for developing guidelines of RA prevention and control in USA.

Genomic instability (GI) was associated with tumorigenesis. However, GI-related lncRNA signature (GILncSig) in lung adenocarcinoma (LUAD) is still unknown. In this study, the lncRNA expression data, somatic mutation information and clinical survival information of LUAD were downloaded from The Cancer Genome Atlas (TCGA) and performed differential analysis. Functional and prognosis analysis revealed that multiple GI-related pathways were enriched. By using univariate and multivariate Cox regression analysis, 5 GI-associated lncRNAs (AC012085.2, FAM83A-AS1, MIR223HG, MIR193BHG, LINC01116) were identified and used to construct a GILncSig model. Mutation burden analysis indicated that the high-risk GI group had much higher somatic mutation count and the risk score constructed by the 5 GI-associated lncRNAs was an independent predictor for overall survival (OS) (P < 0.05). Overall, our study provides valuable insights into the involvement of GI-associated lncRNAs in LUAD and highlights their potential as therapeutic targets.

Background Although observational epidemiological studies have found that smoking is positively associated with risk of rheumatoid arthritis (RA), assessing the causality of this relationship has remained elusive because conventional observational studies are susceptible to bias such as confounding and reverse causation. Here, we applied the Mendelian randomization (MR) approach to examine the potential causal relationship between smoking and risk of RA. Methods Summary statistics data for RA were obtained from a meta-analysis of genome-wide association studies (GWAS), including 14,361 RA cases and 43,923 controls of European ancestry. The instrumental variables (IV) and the genetic association estimates for smoking initiation and lifetime smoking were obtained from a GWAS meta-analysis including 1,232,091 individuals and a GWAS of 462,690 individuals of European ancestry, respectively. MR analyses were performed using the inverse-variance weighted (IVW) method and supplemented with the weighted-median method. Potential pleiotropy was assessed using the MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test and MR-Egger regression. Sensitivity analyses were further performed to test the robustness of the association. Results We found that compared with never smokers, genetic predisposition to smoking initiation was positively associated with risk of RA (odds ratio (OR) = 1.32, 95% confidence interval (CI) = 1.15–1.52, P  = 9.17 × 10 −5 using the IVW method). Similarly, genetically predicted lifetime smoking was associated with an increased risk of RA (OR = 1.55, 95% CI = 1.13–2.14, P  = 0.007). Sensitivity analyses using alternative MR methods and different sets of IVs produced similar results, suggesting the robustness of our findings. Conclusions These results provide support for a causal association between smoking and increased risk of RA. Further studies are warranted to explain the underlying mechanisms of smoking in the development of RA.

BackgroundRheumatoid arthritis (RA) and chronic obstructive pulmonary disease (COPD) are prevalent and incapacitating conditions, sharing common pathogenic pathways such as tobacco use and pulmonary inflammation. The influence of respiratory conditions including COPD on RA has been observed, meanwhile RA may constituting one of the risk factors for COPD. It unclear that whether a bidirectional associate between RA and COPD. Our study aims to explore the bidirectional relationship between RA and COPD.MethodsWe systematically searched PubMed, Cochrane Library, and Embase for observational studies from the databases inception to February 20, 2024, utilizing medical subject headings (MeSH) and keywords. We included studies in which RA and COPD were studied as either exposure or outcome variables. Statistical analyses were conducted employing STATA software (version 14.0). The relationship was reported as odds ratios (OR) and corresponding 95% confidence intervals (CI). Publication bias was assessed using funnel plots and Egger’s regression.ResultsNineteen studies with 1,549,181 participants were included. Risk of bias varied from low to moderate, with evidence levels rated as low or very low. Pooled analysis revealed a significant association between RA and increased COPD risk (OR=1.41, 95%CI 1.13 to 1.76, I2 = 97.8%, P =0.003). Subgroup analyses showed similar COPD risk elevations in both of genders, seropositive/seronegative RA, cohort and case control studies. Additionally, there was a significant RA risk increase among those with COPD (OR=1.36, 95%CI 1.05 to 1.76, I2 = 55.0%, P =0.022), particularly among females and seropositive RA, and cohort studies.ConclusionThe meta-analysis identifies a significant bidirectional association between RA and COPD, emphasizing mutually increased risk. Recognizing this connection may can inform proactive approaches to disease prevention and management, potentially reducing the public health burden and improving quality of life.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024518323.

To advance the integration of traditional Chinese medicine (TCM) with next-generation information technologies, the intelligent identification of Chinese herbal decoction pieces (CHDP) has become a crucial research direction. However, the performance of current algorithms remains unsatisfactory. To address this, we have constructed a diverse CHDP dataset and proposed a lightweight network for CHDP detection, named CHDPL-Net. Based on YOLOv8, this model introduces a new network scaling factor to reduce redundant channels in deep feature maps and optimizes the Neck and Head structures to better accommodate CHDP detection, which primarily involves medium and large targets. Additionally, a newly designed downsampling module, RDown, replaces conventional downsampling methods to reduce computational overhead, while the adopted upsampling module, DySample, significantly enhances the recovery of detailed features. To further improve lightweight performance, we apply GhostConv to optimize the SPPF and C2F modules and incorporate a novel attention mechanism, EHA, which makes the model more sensitive to color and texture information, mitigating the performance degradation caused by lightweight design. Ultimately, CHDPL-Net achieved excellent results with only 31.9% of the Parameters and 30.6% of the FLOPs compared to YOLOv8, obtaining m A P 50 and m A P 50 : 95 scores of 98.2% and 95.4%, respectively, with only a 0.8% performance drop. This demonstrates that the model can meet practical detection needs to a certain extent.